Earwax metabolomics: An innovative pilot metabolic profiling study for assessing metabolic changes in ewes during periparturition period

耳垢代谢组学:一项用于评估围产期母羊代谢变化的创新性试点代谢分析研究

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作者:Engy Shokry, Julião Pereira, Jair Gonzalez Marques Júnior, Paulo Henrique Jorge da Cunha, Antônio Dionísio Feitosa Noronha Filho, Jessica Alves da Silva, Maria Clorinda Soares Fioravanti, Anselmo Elcana de Oliveira, Nelson Roberto Antoniosi Filho

Abstract

Important metabolic changes occur during transition period of late pregnancy and early lactation to meet increasing energy demands of the growing fetus and for milk production. The aim of this investigation is to present an innovative and non-invasive tool using ewe earwax sample analysis to assess the metabolic profile in ewes during late pregnancy and early lactation. In this work, earwax samples were collected from 28 healthy Brazilian Santa Inês ewes divided into 3 sub-groups: 9 non-pregnant ewes, 6 pregnant ewes in the last 30 days of gestation, and 13 lactating ewes ≤ 30 days postpartum. Then, a range of metabolites including volatile organic compounds (VOC), amino acids (AA), and minerals were profiled and quantified in the samples by applying headspace gas chromatography/mass spectrometry, high performance liquid chromatography/tandem mass spectrometry, and inductively coupled plasma-optical emission spectrometry, respectively. As evident in our results, significant changes were observed in the metabolite profile of earwax between the studied groups where a remarkable elevation was detected in the levels of non-esterified fatty acids, alcohols, ketones, and hydroxy urea in the VOC profile of samples obtained from pregnant and lactating ewes. Meanwhile, a significant decrease was detected in the levels of 9 minerals and 14 AA including essential AA (leucine, phenyl alanine, lysine, isoleucine, threonine, valine), conditionally essential AA (arginine, glycine, tyrosine, proline, serine), and a non-essential AA (alanine). Multivariate analysis using robust principal component analysis and hierarchical cluster analysis was successfully applied to discriminate the three study groups using the variations of metabolites in the two stress states (pregnancy and lactation) from the healthy non-stress condition. The innovative developed method was successful in evaluating pre- and post-parturient metabolic changes using earwax and can in the future be applied to recognize markers for diagnosis, prevention, and intervention of pregnancy complications in ewes.

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